Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations591
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.1 KiB
Average record size in memory104.2 B

Variable types

Categorical4
Text2
Numeric7

Alerts

Calorias is highly overall correlated with Lipídeos and 1 other fieldsHigh correlation
Carboidrato is highly overall correlated with ColesterolHigh correlation
Colesterol is highly overall correlated with Carboidrato and 3 other fieldsHigh correlation
Lipídeos is highly overall correlated with Calorias and 4 other fieldsHigh correlation
Proteína is highly overall correlated with Colesterol and 2 other fieldsHigh correlation
Sódio is highly overall correlated with Colesterol and 2 other fieldsHigh correlation
Umidade is highly overall correlated with Calorias and 1 other fieldsHigh correlation
Numero is highly imbalanced (97.3%) Imbalance
Alimento has unique values Unique
Umidade has 10 (1.7%) zeros Zeros
Proteína has 31 (5.2%) zeros Zeros
Lipídeos has 47 (8.0%) zeros Zeros
Colesterol has 341 (57.7%) zeros Zeros
Carboidrato has 154 (26.1%) zeros Zeros
Sódio has 74 (12.5%) zeros Zeros

Reproduction

Analysis started2024-11-07 03:52:14.448735
Analysis finished2024-11-07 03:52:19.994389
Duration5.55 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Numero
Categorical

Imbalance 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
0
588 
1
 
1
17
 
1
Alimentos preparados
 
1

Length

Max length20
Median length1
Mean length1.0338409
Min length1

Characters and Unicode

Total characters611
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.5%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 588
99.5%
1 1
 
0.2%
17 1
 
0.2%
Alimentos preparados 1
 
0.2%

Length

2024-11-07T00:52:20.072445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T00:52:20.150570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 588
99.3%
1 1
 
0.2%
17 1
 
0.2%
alimentos 1
 
0.2%
preparados 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 588
96.2%
1 2
 
0.3%
e 2
 
0.3%
s 2
 
0.3%
a 2
 
0.3%
p 2
 
0.3%
r 2
 
0.3%
o 2
 
0.3%
A 1
 
0.2%
n 1
 
0.2%
Other values (7) 7
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 588
96.2%
1 2
 
0.3%
e 2
 
0.3%
s 2
 
0.3%
a 2
 
0.3%
p 2
 
0.3%
r 2
 
0.3%
o 2
 
0.3%
A 1
 
0.2%
n 1
 
0.2%
Other values (7) 7
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 588
96.2%
1 2
 
0.3%
e 2
 
0.3%
s 2
 
0.3%
a 2
 
0.3%
p 2
 
0.3%
r 2
 
0.3%
o 2
 
0.3%
A 1
 
0.2%
n 1
 
0.2%
Other values (7) 7
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 588
96.2%
1 2
 
0.3%
e 2
 
0.3%
s 2
 
0.3%
a 2
 
0.3%
p 2
 
0.3%
r 2
 
0.3%
o 2
 
0.3%
A 1
 
0.2%
n 1
 
0.2%
Other values (7) 7
 
1.1%

Alimento
Text

Unique 

Distinct591
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2024-11-07T00:52:20.400624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length64
Median length45
Mean length23.043993
Min length1

Characters and Unicode

Total characters13619
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique591 ?
Unique (%)100.0%

Sample

1st rowArroz, integral, cozido
2nd rowArroz, integral, cru
3rd rowArroz, tipo 1, cozido
4th rowArroz, tipo 1, cru
5th rowArroz, tipo 2, cozido
ValueCountFrequency (%)
cru 118
 
5.8%
crua 107
 
5.3%
de 87
 
4.3%
carne 65
 
3.2%
bovina 59
 
2.9%
com 52
 
2.6%
sem 46
 
2.3%
gordura 38
 
1.9%
frango 33
 
1.6%
cozido 33
 
1.6%
Other values (523) 1386
68.5%
2024-11-07T00:52:20.817516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1617
 
11.9%
1439
 
10.6%
o 1141
 
8.4%
, 1070
 
7.9%
r 957
 
7.0%
e 877
 
6.4%
c 702
 
5.2%
i 664
 
4.9%
n 541
 
4.0%
d 466
 
3.4%
Other values (67) 4145
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13619
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1617
 
11.9%
1439
 
10.6%
o 1141
 
8.4%
, 1070
 
7.9%
r 957
 
7.0%
e 877
 
6.4%
c 702
 
5.2%
i 664
 
4.9%
n 541
 
4.0%
d 466
 
3.4%
Other values (67) 4145
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13619
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1617
 
11.9%
1439
 
10.6%
o 1141
 
8.4%
, 1070
 
7.9%
r 957
 
7.0%
e 877
 
6.4%
c 702
 
5.2%
i 664
 
4.9%
n 541
 
4.0%
d 466
 
3.4%
Other values (67) 4145
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13619
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1617
 
11.9%
1439
 
10.6%
o 1141
 
8.4%
, 1070
 
7.9%
r 957
 
7.0%
e 877
 
6.4%
c 702
 
5.2%
i 664
 
4.9%
n 541
 
4.0%
d 466
 
3.4%
Other values (67) 4145
30.4%

Umidade
Real number (ℝ)

High correlation  Zeros 

Distinct579
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.341517
Minimum0
Maximum99.608667
Zeros10
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-11-07T00:52:20.949211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6986667
Q140.992833
median69.018
Q385.006667
95-th percentile93.835667
Maximum99.608667
Range99.608667
Interquartile range (IQR)44.013833

Descriptive statistics

Standard deviation30.222918
Coefficient of variation (CV)0.50930477
Kurtosis-0.79867031
Mean59.341517
Median Absolute Deviation (MAD)18.600333
Skewness-0.72331269
Sum35070.837
Variance913.42476
MonotonicityNot monotonic
2024-11-07T00:52:21.058361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
1.7%
2.93 2
 
0.3%
15.82333333 2
 
0.3%
74.95133333 2
 
0.3%
13.2245 1
 
0.2%
68.72766667 1
 
0.2%
13.16475 1
 
0.2%
9.133333333 1
 
0.2%
3.216666667 1
 
0.2%
2.183333333 1
 
0.2%
Other values (569) 569
96.3%
ValueCountFrequency (%)
0 10
1.7%
0.05 1
 
0.2%
0.12 1
 
0.2%
0.4133333333 1
 
0.2%
0.5966666667 1
 
0.2%
0.969 1
 
0.2%
1 1
 
0.2%
1.021666667 1
 
0.2%
1.103333333 1
 
0.2%
1.17 1
 
0.2%
ValueCountFrequency (%)
99.60866667 1
0.2%
99.37033333 1
0.2%
99.305 1
0.2%
97.372 1
0.2%
97.16866667 1
0.2%
96.78666667 1
0.2%
96.09333333 1
0.2%
95.87 1
0.2%
95.72833333 1
0.2%
95.69 1
0.2%

Calorias
Real number (ℝ)

High correlation 

Distinct328
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.47547
Minimum0
Maximum884
Zeros3
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-11-07T00:52:21.179703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.5
Q157
median147
Q3300.5
95-th percentile502.5
Maximum884
Range884
Interquartile range (IQR)243.5

Descriptive statistics

Standard deviation170.35182
Coefficient of variation (CV)0.87147416
Kurtosis2.663445
Mean195.47547
Median Absolute Deviation (MAD)107
Skewness1.4262612
Sum115526
Variance29019.741
MonotonicityNot monotonic
2024-11-07T00:52:21.289583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
884 8
 
1.4%
45 7
 
1.2%
36 7
 
1.2%
13 6
 
1.0%
29 6
 
1.0%
19 6
 
1.0%
30 6
 
1.0%
38 6
 
1.0%
41 6
 
1.0%
51 5
 
0.8%
Other values (318) 528
89.3%
ValueCountFrequency (%)
0 3
0.5%
1 1
 
0.2%
2 2
 
0.3%
8 1
 
0.2%
9 2
 
0.3%
10 1
 
0.2%
12 2
 
0.3%
13 6
1.0%
14 1
 
0.2%
15 2
 
0.3%
ValueCountFrequency (%)
884 8
1.4%
757 1
 
0.2%
725 1
 
0.2%
722 1
 
0.2%
696 1
 
0.2%
642 1
 
0.2%
620 1
 
0.2%
605 1
 
0.2%
596 1
 
0.2%
594 1
 
0.2%

Proteína
Real number (ℝ)

High correlation  Zeros 

Distinct547
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.901003
Minimum0
Maximum36.45
Zeros31
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-11-07T00:52:21.398963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.0841033
median5.64375
Q318.352083
95-th percentile30.18125
Maximum36.45
Range36.45
Interquartile range (IQR)17.26798

Descriptive statistics

Standard deviation10.310427
Coefficient of variation (CV)1.0413518
Kurtosis-0.53211267
Mean9.901003
Median Absolute Deviation (MAD)5.0640399
Skewness0.851216
Sum5851.4928
Variance106.30491
MonotonicityNot monotonic
2024-11-07T00:52:21.547395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
5.2%
0.4145833333 2
 
0.3%
0.7666666667 2
 
0.3%
0.90625 2
 
0.3%
2.133333333 2
 
0.3%
0.4083333333 2
 
0.3%
2.360600042 2
 
0.3%
1.391304348 2
 
0.3%
0.4041666667 2
 
0.3%
1.291666667 2
 
0.3%
Other values (537) 542
91.7%
ValueCountFrequency (%)
0 31
5.2%
0.08958333333 1
 
0.2%
0.19375 1
 
0.2%
0.225 1
 
0.2%
0.2354166667 1
 
0.2%
0.2854166667 1
 
0.2%
0.2866666667 1
 
0.2%
0.3083333333 1
 
0.2%
0.3166666667 1
 
0.2%
0.32 2
 
0.3%
ValueCountFrequency (%)
36.45 1
0.2%
36.36458333 1
0.2%
36.03010024 1
0.2%
35.9 1
0.2%
35.88333333 1
0.2%
35.725 1
0.2%
35.68750024 1
0.2%
35.55361333 1
0.2%
35.06333333 1
0.2%
34.69 1
0.2%

Lipídeos
Real number (ℝ)

High correlation  Zeros 

Distinct513
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1737416
Minimum0
Maximum100
Zeros47
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-11-07T00:52:21.664215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.20716667
median2.294
Q311.9325
95-th percentile33.650167
Maximum100
Range100
Interquartile range (IQR)11.725333

Descriptive statistics

Standard deviation16.407895
Coefficient of variation (CV)1.7885718
Kurtosis14.10062
Mean9.1737416
Median Absolute Deviation (MAD)2.294
Skewness3.4351967
Sum5421.6813
Variance269.21902
MonotonicityNot monotonic
2024-11-07T00:52:21.789274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47
 
8.0%
100 8
 
1.4%
0.07333333333 5
 
0.8%
0.06 3
 
0.5%
0.22 3
 
0.5%
0.1733333333 3
 
0.5%
0.15 2
 
0.3%
0.19 2
 
0.3%
0.2133333333 2
 
0.3%
0.1033333333 2
 
0.3%
Other values (503) 514
87.0%
ValueCountFrequency (%)
0 47
8.0%
0.05 1
 
0.2%
0.052 1
 
0.2%
0.05333333333 1
 
0.2%
0.06 3
 
0.5%
0.06366666667 1
 
0.2%
0.065 1
 
0.2%
0.06666666667 2
 
0.3%
0.06733333333 1
 
0.2%
0.069 1
 
0.2%
ValueCountFrequency (%)
100 8
1.4%
86.03933333 1
 
0.2%
82.361 1
 
0.2%
81.73366667 1
 
0.2%
67.43433333 1
 
0.2%
67.24566667 1
 
0.2%
67.097 1
 
0.2%
64.30866667 1
 
0.2%
63.459 1
 
0.2%
60.25666667 1
 
0.2%

Colesterol
Real number (ℝ)

High correlation  Zeros 

Distinct249
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.616051
Minimum0
Maximum1272.372
Zeros341
Zeros (%)57.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-11-07T00:52:21.898597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q360.24
95-th percentile143.939
Maximum1272.372
Range1272.372
Interquartile range (IQR)60.24

Descriptive statistics

Standard deviation87.539776
Coefficient of variation (CV)2.2097047
Kurtosis73.88592
Mean39.616051
Median Absolute Deviation (MAD)0
Skewness6.6985909
Sum23413.086
Variance7663.2124
MonotonicityNot monotonic
2024-11-07T00:52:22.046402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 341
57.7%
55.61 2
 
0.3%
50.3 2
 
0.3%
1.267333333 1
 
0.2%
73.204 1
 
0.2%
81.68733333 1
 
0.2%
1.350666667 1
 
0.2%
76.79766667 1
 
0.2%
63.20766667 1
 
0.2%
17.568 1
 
0.2%
Other values (239) 239
40.4%
ValueCountFrequency (%)
0 341
57.7%
1.267333333 1
 
0.2%
1.350666667 1
 
0.2%
1.85166 1
 
0.2%
2.112666667 1
 
0.2%
2.220333333 1
 
0.2%
3.331666667 1
 
0.2%
4.103333333 1
 
0.2%
4.742 1
 
0.2%
5.36975 1
 
0.2%
ValueCountFrequency (%)
1272.372 1
0.2%
601.47 1
0.2%
568 1
0.2%
516.2636667 1
0.2%
396.5716667 1
0.2%
392.883 1
0.2%
383.8253333 1
0.2%
355.94 1
0.2%
340.58 1
0.2%
315.3573333 1
0.2%

Carboidrato
Real number (ℝ)

High correlation  Zeros 

Distinct438
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.101371
Minimum-0.045
Maximum99.61
Zeros154
Zeros (%)26.1%
Negative4
Negative (%)0.7%
Memory size4.7 KiB
2024-11-07T00:52:22.189523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.045
5-th percentile0
Q10
median7.7116667
Q323.4697
95-th percentile79.126117
Maximum99.61
Range99.655
Interquartile range (IQR)23.4697

Descriptive statistics

Standard deviation25.86898
Coefficient of variation (CV)1.3542997
Kurtosis0.93780918
Mean19.101371
Median Absolute Deviation (MAD)7.7116667
Skewness1.4865861
Sum11288.91
Variance669.20411
MonotonicityNot monotonic
2024-11-07T00:52:22.421236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 154
 
26.1%
84.71391304 1
 
0.2%
47.864 1
 
0.2%
54.71775 1
 
0.2%
52.276 1
 
0.2%
45.10883333 1
 
0.2%
78.061 1
 
0.2%
23.62773329 1
 
0.2%
80.835 1
 
0.2%
80.44833333 1
 
0.2%
Other values (428) 428
72.4%
ValueCountFrequency (%)
-0.045 1
 
0.2%
-0.02666666667 1
 
0.2%
-0.02333333333 1
 
0.2%
-0.006666666667 1
 
0.2%
0 154
26.1%
0.02 1
 
0.2%
0.05833333333 1
 
0.2%
0.06329999256 1
 
0.2%
0.23625 1
 
0.2%
0.3913333333 1
 
0.2%
ValueCountFrequency (%)
99.61 1
0.2%
99.54 1
0.2%
94.45 1
0.2%
91.17666667 1
0.2%
90.79241667 1
0.2%
89.33666667 1
0.2%
89.22333333 1
0.2%
89.19416667 1
0.2%
88.84057971 1
0.2%
87.89898551 1
0.2%

Cálcio
Text

Distinct572
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2024-11-07T00:52:22.638704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length13.883249
Min length1

Characters and Unicode

Total characters8205
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique568 ?
Unique (%)96.1%

Sample

1st row5.204
2nd row7.818000000000001
3rd row3.544333333333334
4th row4.414333333333333
5th row3.3336666666666663
ValueCountFrequency (%)
0 17
 
2.9%
4.026666666666666 2
 
0.3%
3.533333333333333 2
 
0.3%
10.324666666666667 2
 
0.3%
2.8253333333333335 1
 
0.2%
19.244 1
 
0.2%
17.453333333333333 1
 
0.2%
9.971666666666666 1
 
0.2%
105.30633333333333 1
 
0.2%
5.204 1
 
0.2%
Other values (561) 561
95.1%
2024-11-07T00:52:22.919551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 2650
32.3%
6 2195
26.8%
. 570
 
6.9%
9 437
 
5.3%
1 422
 
5.1%
0 398
 
4.9%
2 331
 
4.0%
7 329
 
4.0%
5 317
 
3.9%
4 314
 
3.8%
Other values (2) 242
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2650
32.3%
6 2195
26.8%
. 570
 
6.9%
9 437
 
5.3%
1 422
 
5.1%
0 398
 
4.9%
2 331
 
4.0%
7 329
 
4.0%
5 317
 
3.9%
4 314
 
3.8%
Other values (2) 242
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2650
32.3%
6 2195
26.8%
. 570
 
6.9%
9 437
 
5.3%
1 422
 
5.1%
0 398
 
4.9%
2 331
 
4.0%
7 329
 
4.0%
5 317
 
3.9%
4 314
 
3.8%
Other values (2) 242
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2650
32.3%
6 2195
26.8%
. 570
 
6.9%
9 437
 
5.3%
1 422
 
5.1%
0 398
 
4.9%
2 331
 
4.0%
7 329
 
4.0%
5 317
 
3.9%
4 314
 
3.8%
Other values (2) 242
 
2.9%

Sódio
Real number (ℝ)

High correlation  Zeros 

Distinct516
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean469.48503
Minimum0
Maximum39943.203
Zeros74
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-11-07T00:52:23.044381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.8883333
median38.783333
Q3120.01867
95-th percentile1193.9447
Maximum39943.203
Range39943.203
Interquartile range (IQR)118.13033

Descriptive statistics

Standard deviation2765.1421
Coefficient of variation (CV)5.8897343
Kurtosis121.61439
Mean469.48503
Median Absolute Deviation (MAD)37.801333
Skewness10.51614
Sum277465.65
Variance7646010.8
MonotonicityNot monotonic
2024-11-07T00:52:23.153765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
 
12.5%
3.333333333 2
 
0.3%
114.9053333 2
 
0.3%
1.200666667 1
 
0.2%
1.019166667 1
 
0.2%
235.7096667 1
 
0.2%
206.7693333 1
 
0.2%
1.959666667 1
 
0.2%
0.5688333333 1
 
0.2%
4.626666667 1
 
0.2%
Other values (506) 506
85.6%
ValueCountFrequency (%)
0 74
12.5%
0.3553333333 1
 
0.2%
0.5006666667 1
 
0.2%
0.5203333333 1
 
0.2%
0.5513333333 1
 
0.2%
0.5688333333 1
 
0.2%
0.5913333333 1
 
0.2%
0.5966666667 1
 
0.2%
0.6253333333 1
 
0.2%
0.629 1
 
0.2%
ValueCountFrequency (%)
39943.203 1
0.2%
32560 1
0.2%
23431.52167 1
0.2%
22299.90033 1
0.2%
22179.66667 1
0.2%
13585.05667 1
0.2%
10052.41133 1
0.2%
5875.029 1
0.2%
5024.208333 1
0.2%
4439.55 1
0.2%

Tipo
Categorical

Distinct15
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Carnes e derivados
122 
Verduras, hortaliças e derivados
99 
Frutas e derivados
96 
Cereais e derivados
62 
Pescados e frutos do mar
49 
Other values (10)
163 

Length

Max length37
Median length33
Mean length21.676819
Min length11

Characters and Unicode

Total characters12811
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCereais e derivados
2nd rowCereais e derivados
3rd rowCereais e derivados
4th rowCereais e derivados
5th rowCereais e derivados

Common Values

ValueCountFrequency (%)
Carnes e derivados 122
20.6%
Verduras, hortaliças e derivados 99
16.8%
Frutas e derivados 96
16.2%
Cereais e derivados 62
10.5%
Pescados e frutos do mar 49
8.3%
Alimentos preparados 33
 
5.6%
Leguminosas e derivados 30
 
5.1%
Leite e derivados 21
 
3.6%
Produtos açucarados 20
 
3.4%
Bebidas (alcoólicas e não alcoólicas) 14
 
2.4%
Other values (5) 45
 
7.6%

Length

2024-11-07T00:52:23.280110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e 524
27.2%
derivados 437
22.7%
carnes 122
 
6.3%
verduras 99
 
5.1%
hortaliças 99
 
5.1%
frutas 96
 
5.0%
cereais 62
 
3.2%
pescados 49
 
2.5%
frutos 49
 
2.5%
do 49
 
2.5%
Other values (18) 341
17.7%

Most occurring characters

ValueCountFrequency (%)
e 1584
12.4%
a 1376
10.7%
s 1373
10.7%
1336
10.4%
r 1256
9.8%
d 1182
9.2%
o 941
7.3%
i 753
 
5.9%
v 444
 
3.5%
t 343
 
2.7%
Other values (29) 2223
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12811
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1584
12.4%
a 1376
10.7%
s 1373
10.7%
1336
10.4%
r 1256
9.8%
d 1182
9.2%
o 941
7.3%
i 753
 
5.9%
v 444
 
3.5%
t 343
 
2.7%
Other values (29) 2223
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12811
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1584
12.4%
a 1376
10.7%
s 1373
10.7%
1336
10.4%
r 1256
9.8%
d 1182
9.2%
o 941
7.3%
i 753
 
5.9%
v 444
 
3.5%
t 343
 
2.7%
Other values (29) 2223
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12811
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1584
12.4%
a 1376
10.7%
s 1373
10.7%
1336
10.4%
r 1256
9.8%
d 1182
9.2%
o 941
7.3%
i 753
 
5.9%
v 444
 
3.5%
t 343
 
2.7%
Other values (29) 2223
17.4%

Doencas_Restritivas
Categorical

Distinct10
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Hipertensão
102 
Diabetes Tipo 2
91 
Obesidade
87 
Diabetes Tipo 1
82 
Osteoporose
46 
Other values (5)
183 

Length

Max length19
Median length17
Mean length12.978003
Min length7

Characters and Unicode

Total characters7670
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOsteoporose
2nd rowOsteoporose
3rd rowDoença Celíaca
4th rowInsuficiência Renal
5th rowObesidade

Common Values

ValueCountFrequency (%)
Hipertensão 102
17.3%
Diabetes Tipo 2 91
15.4%
Obesidade 87
14.7%
Diabetes Tipo 1 82
13.9%
Osteoporose 46
7.8%
Colesterol Alto 44
7.4%
Doenças Cardíacas 37
 
6.3%
Insuficiência Renal 37
 
6.3%
Nenhuma 34
 
5.8%
Doença Celíaca 31
 
5.2%

Length

2024-11-07T00:52:23.373863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T00:52:23.497663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
diabetes 173
15.9%
tipo 173
15.9%
hipertensão 102
9.4%
2 91
8.4%
obesidade 87
8.0%
1 82
 
7.6%
osteoporose 46
 
4.2%
colesterol 44
 
4.1%
alto 44
 
4.1%
doenças 37
 
3.4%
Other values (6) 207
19.1%

Most occurring characters

ValueCountFrequency (%)
e 1074
14.0%
i 646
 
8.4%
o 613
 
8.0%
a 609
 
7.9%
s 609
 
7.9%
495
 
6.5%
t 409
 
5.3%
p 321
 
4.2%
n 315
 
4.1%
b 260
 
3.4%
Other values (23) 2319
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1074
14.0%
i 646
 
8.4%
o 613
 
8.0%
a 609
 
7.9%
s 609
 
7.9%
495
 
6.5%
t 409
 
5.3%
p 321
 
4.2%
n 315
 
4.1%
b 260
 
3.4%
Other values (23) 2319
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1074
14.0%
i 646
 
8.4%
o 613
 
8.0%
a 609
 
7.9%
s 609
 
7.9%
495
 
6.5%
t 409
 
5.3%
p 321
 
4.2%
n 315
 
4.1%
b 260
 
3.4%
Other values (23) 2319
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1074
14.0%
i 646
 
8.4%
o 613
 
8.0%
a 609
 
7.9%
s 609
 
7.9%
495
 
6.5%
t 409
 
5.3%
p 321
 
4.2%
n 315
 
4.1%
b 260
 
3.4%
Other values (23) 2319
30.2%

Refeicao_Indicada
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Jantar
168 
Lanche da Tarde
153 
Almoço
137 
Café da Manhã
133 

Length

Max length15
Median length6
Mean length9.9052453
Min length6

Characters and Unicode

Total characters5854
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCafé da Manhã
2nd rowLanche da Tarde
3rd rowCafé da Manhã
4th rowAlmoço
5th rowAlmoço

Common Values

ValueCountFrequency (%)
Jantar 168
28.4%
Lanche da Tarde 153
25.9%
Almoço 137
23.2%
Café da Manhã 133
22.5%

Length

2024-11-07T00:52:23.606984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-07T00:52:23.700775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
da 286
24.6%
jantar 168
14.4%
lanche 153
13.2%
tarde 153
13.2%
almoço 137
11.8%
café 133
11.4%
manhã 133
11.4%

Most occurring characters

ValueCountFrequency (%)
a 1194
20.4%
572
 
9.8%
n 454
 
7.8%
d 439
 
7.5%
r 321
 
5.5%
e 306
 
5.2%
h 286
 
4.9%
o 274
 
4.7%
J 168
 
2.9%
t 168
 
2.9%
Other values (12) 1672
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5854
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1194
20.4%
572
 
9.8%
n 454
 
7.8%
d 439
 
7.5%
r 321
 
5.5%
e 306
 
5.2%
h 286
 
4.9%
o 274
 
4.7%
J 168
 
2.9%
t 168
 
2.9%
Other values (12) 1672
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5854
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1194
20.4%
572
 
9.8%
n 454
 
7.8%
d 439
 
7.5%
r 321
 
5.5%
e 306
 
5.2%
h 286
 
4.9%
o 274
 
4.7%
J 168
 
2.9%
t 168
 
2.9%
Other values (12) 1672
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5854
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1194
20.4%
572
 
9.8%
n 454
 
7.8%
d 439
 
7.5%
r 321
 
5.5%
e 306
 
5.2%
h 286
 
4.9%
o 274
 
4.7%
J 168
 
2.9%
t 168
 
2.9%
Other values (12) 1672
28.6%

Interactions

2024-11-07T00:52:18.931179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:14.881492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:15.691645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:16.348293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:16.975724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:17.649289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:18.274621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:19.024987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:14.981555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:15.788905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:16.443087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:17.069442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:17.743072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:18.368409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:19.118739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:15.065282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:15.863661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:16.521247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:17.154168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:17.821218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:18.462119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:19.322509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:15.215593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:15.957437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:16.599768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:17.257855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:17.914912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:18.571840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:19.416255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:15.391872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:16.051195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:16.693533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:17.367227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:18.008723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:18.649930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:19.525578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:15.503826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:16.144910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:16.789872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:17.461824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:18.102487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:18.759337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:19.603702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:15.597878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:16.238920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:16.865875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:17.539968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:18.180871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-07T00:52:18.837449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-07T00:52:23.782344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CaloriasCarboidratoColesterolDoencas_RestritivasLipídeosNumeroProteínaRefeicao_IndicadaSódioTipoUmidade
Calorias1.0000.2360.3020.0000.7240.0000.4340.0540.4110.457-0.938
Carboidrato0.2361.000-0.6020.000-0.2540.089-0.3780.065-0.1820.350-0.307
Colesterol0.302-0.6021.0000.0240.5770.0000.7390.0000.5670.326-0.203
Doencas_Restritivas0.0000.0000.0241.0000.0000.0000.0130.0280.0000.0000.000
Lipídeos0.724-0.2540.5770.0001.0000.0000.6090.0770.5510.415-0.591
Numero0.0000.0890.0000.0000.0001.0000.0000.0000.0000.0000.079
Proteína0.434-0.3780.7390.0130.6090.0001.0000.0000.5380.382-0.366
Refeicao_Indicada0.0540.0650.0000.0280.0770.0000.0001.0000.0280.0690.024
Sódio0.411-0.1820.5670.0000.5510.0000.5380.0281.0000.223-0.395
Tipo0.4570.3500.3260.0000.4150.0000.3820.0690.2231.0000.385
Umidade-0.938-0.307-0.2030.000-0.5910.079-0.3660.024-0.3950.3851.000

Missing values

2024-11-07T00:52:19.744361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-07T00:52:19.916194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NumeroAlimentoUmidadeCaloriasProteínaLipídeosColesterolCarboidratoCálcioSódioTipoDoencas_RestritivasRefeicao_Indicada
01Arroz, integral, cozido70.1386671232.5882501.0003330.025.8097505.2041.244667Cereais e derivadosOsteoporoseCafé da Manhã
10Arroz, integral, cru12.1798333597.3232861.8648330.077.4507147.8180000000000011.645667Cereais e derivadosOsteoporoseLanche da Tarde
20Arroz, tipo 1, cozido69.1136671282.5208170.2270000.028.0598503.5443333333333341.200667Cereais e derivadosDoença CelíacaCafé da Manhã
30Arroz, tipo 1, cru13.2245003577.1585400.3350000.078.7595434.4143333333333331.019167Cereais e derivadosInsuficiência RenalAlmoço
40Arroz, tipo 2, cozido68.7276671302.5684170.3616670.028.1925833.33366666666666631.959667Cereais e derivadosObesidadeAlmoço
50Arroz, tipo 2, cru13.1647503587.2418830.2755000.078.8814504.83350.568833Cereais e derivadosObesidadeCafé da Manhã
60Aveia, flocos, crua9.13333339313.9210268.4966670.066.63564147.894.626667Cereais e derivadosDiabetes Tipo 1Café da Manhã
70Biscoito, doce, maisena3.2166674428.07252211.9666670.075.23414554.45352.026667Cereais e derivadosInsuficiência RenalJantar
80Biscoito, doce, recheado com chocolate2.1833334716.39721719.5833330.070.54944927.23239.200000Cereais e derivadosNenhumaCafé da Manhã
90Biscoito, doce, recheado com morango2.7333334715.71982619.5733330.071.01350735.78229.816667Cereais e derivadosDoenças CardíacasCafé da Manhã
NumeroAlimentoUmidadeCaloriasProteínaLipídeosColesterolCarboidratoCálcioSódioTipoDoencas_RestritivasRefeicao_Indicada
5810Amêndoa, torrada, salgada3.10600058018.55475947.3243330.029.547240236.70433333333335278.522667Nozes e sementesDoenças CardíacasCafé da Manhã
5820Castanha-de-caju, torrada, salgada3.46400057018.50936746.2796670.029.13496632.58766666666667125.000000Nozes e sementesHipertensãoLanche da Tarde
5830Castanha-do-Brasil, crua3.52400064214.53634063.4590000.015.078660146.336666666666670.654000Nozes e sementesNenhumaCafé da Manhã
5840Coco, cru42.9611674063.69183441.9763330.010.4016666.48450000000000115.320000Nozes e sementesHipertensãoJantar
5850Farinha, de mesocarpo de babaçu, crua15.8233333281.4062670.1980000.079.17306760.95233333333333512.463000Nozes e sementesInsuficiência RenalLanche da Tarde
5860Gergelim, semente3.85933358321.16466750.4326670.021.61766600.000000Nozes e sementesDiabetes Tipo 1Almoço
5870Linhaça, semente6.68300049514.08386732.2529330.043.312199211.497666666666658.673333Nozes e sementesInsuficiência RenalJantar
5880Pinhão, cozido50.5133331742.9803670.7470000.043.91763315.7673333333333330.862667Nozes e sementesNenhumaLanche da Tarde
5890Pupunha, cozida54.4600002182.52291712.7616670.029.56941727.586333333333330.908667Nozes e sementesObesidadeLanche da Tarde
5900Noz, crua6.24466762013.97080159.3596670.018.363866105.306333333333334.570667Nozes e sementesDiabetes Tipo 2Lanche da Tarde